"""simple docstring"""


# using dfs for finding eulerian path traversal










def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=None							)				->   Dict:




						'''simple docstring'''



						lowerCamelCase__   =(path or []) + [u]
						for v in graph[u]:
												if visited_edge[u][v] is False:
																		lowerCamelCase__   , lowerCamelCase__   =True, True
																		lowerCamelCase__   =dfs(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						return path





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =0
						lowerCamelCase__   =-1
						for i in range(__lowerCAmelCase							):
												if i not in graph.keys():
																		continue
												if len(graph[i]							) % 2 == 1:
																		odd_degree_nodes += 1
																		lowerCamelCase__   =i
						if odd_degree_nodes == 0:
												return 1, odd_node
						if odd_degree_nodes == 2:
												return 2, odd_node
						return 3, odd_node





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =[[False for _ in range(max_node + 1							)] for _ in range(max_node + 1							)]
						lowerCamelCase__   , lowerCamelCase__   =check_circuit_or_path(__lowerCAmelCase  ,       __lowerCAmelCase							)
						if check == 3:
												print("graph is not Eulerian"							)
												print("no path"							)
												return
						lowerCamelCase__   =1
						if check == 2:
												lowerCamelCase__   =odd_node
												print("graph has a Euler path"							)
						if check == 1:
												print("graph has a Euler cycle"							)
						lowerCamelCase__   =dfs(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						print(__lowerCAmelCase							)





def      lowerCamelCase_					(   )				->   str:




						'''simple docstring'''



						lowerCamelCase__   ={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
						lowerCamelCase__   ={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
						lowerCamelCase__   ={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
						lowerCamelCase__   ={1: [2, 3], 2: [1, 3], 3: [1, 2]}
						lowerCamelCase__   ={
						    1: [],
						    2: []
						    # all degree is zero
						}
						lowerCamelCase__   =10
						check_euler(__lowerCAmelCase  ,       __lowerCAmelCase							)
						check_euler(__lowerCAmelCase  ,       __lowerCAmelCase							)
						check_euler(__lowerCAmelCase  ,       __lowerCAmelCase							)
						check_euler(__lowerCAmelCase  ,       __lowerCAmelCase							)
						check_euler(__lowerCAmelCase  ,       __lowerCAmelCase							)


if __name__ == "__main__":
	main()



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings